\section{The Proposal: Generating Subjective Responses}
\subsection{Our Approach}\label{sec:approach}

Natural language is, above all, a   communicative device that we employ to achieve certain goals.
In social media, the driving force behind generating responses is a  responder's disposition  towards some topic.
This topic could be a political campaign or a candidate, a product, or some abstract idea, which the responder has a motive to promote.
Let us call this goal our user's {\em agenda}.

User response generation, like any other natural language utterance generation, is   triggered  by a certain event that is related  to the communicative goal.
In a social media setting, this event is often  a new online {\em document}. The document and the agenda thus form the input to our generation system.
%The document, together with the the user disposition, or agenda, is then the input to our system.
%\[
%(i.e., \(input=\langle document,agenda\rangle\)).
Each   document and  each agenda contain (possibly many) topics, each of which is associated with a (positive or negative) sentiment. Document sentiments are attributed to the author, whereas agenda sentiments are attributed to the user (henceforth: the responder).
%
%To emulate this, we envision the following life cycle of an automatic  response-generation system: Each   document and each  agenda contain (possibly many) topics, associated with subjectivity measures, attributed to the author or the reader.
%
%was removed by Stefan - might be important: [For each new document that appears online]
%The  system  analyzes the topics of the document  and the authors' sentiment. It then  performs an action of {\em intersection} on the document's topics and  topics in the user agenda. If the document is relevant to the agenda (i.e., a non-empty intersection), the system generates a response.  If the intersection is empty  (the document is irrelevant)  the response is null (and, in practice, not generated).
%
%The generation system  analyzes the topic of the document  and the authors' sentiment. Based on the responder disposition, and possibly extra  knowledge that is associated with, the system  generates a relevant response.

For each non-empty intersection of the topics in the document and in the agenda, our response-generation system aims to generate utterances that are fluent, human-like, and effectively engage readers.
The generation is based on three assumptions, roughly reflecting  the Gricean  maxims of cooperative interaction \cite{grice}. Online user responses should then be:
\begin{itemize}
\item  {\em Economic} (Maxim of Quantity):\ Responses are brief and concise;
\item  {\em Relevant} (Maxim of Relation):\ Responses directly address the documents' content.
\item  {\em Opinionated} (Maxim of Quality):\ Responses express responder’s beliefs, sentiments, or dispositions towards the topic(s).
\end{itemize}

%Commented out by Stefan (is it not important to state?): [The generation system has an additional, and a rather basic, goal (which would be trivial for human): that is, to generate responses that are fluent and present human-like language that effectively engage human readers.]
